CuePD: An IoT Approach for Enhancing Gait Rehabilitation in Older Adults Through Personalized Music Cueing

Abstract

Falls in people with Parkinson's disease (PwPD) under- score the need for precise sensing tools to robustly assess gait and deliver tailored rehabilitation. Using wearable inertial measurement units (IMUs) offers a practical alternative to assess gait and intervene in any location. This study develops a robust and innovative smartphone application/app that uses embedded IMU for real-time gait sensing to facilitate personalized cueing for targeted rehabilitation to reduce falls. Here, older adults had their CuePD -based gait validated against a reference standard and were then exposed to different but personalized cueing modalities to target a 10.0% increase in cadence. CuePD increased cadence by 8.3% and showed robust agreement with the reference before and after cueing as evidenced by strong Pearson correlation coefficients (≥0.843) and intraclass correlation coefficients (≥0.845) across clinically relevant temporal gait characteristics (e.g., step time). Gait sensing via a smartphone is robust and CuePD indicates the feasibility of a scalable and personalized approach for targeted gait rehabilitation. Future research will extend to PwPD.